Overview

Dataset statistics

Number of variables28
Number of observations119143
Missing cells20754
Missing cells (%)0.6%
Duplicate rows7779
Duplicate rows (%)6.5%
Total size in memory30.4 MiB
Average record size in memory267.5 B

Variable types

Categorical16
Numeric12

Alerts

Dataset has 7779 (6.5%) duplicate rowsDuplicates
order_purchase_timestamp has a high cardinality: 98875 distinct valuesHigh cardinality
order_approved_at has a high cardinality: 90733 distinct valuesHigh cardinality
order_delivered_carrier_date has a high cardinality: 81018 distinct valuesHigh cardinality
order_delivered_customer_date has a high cardinality: 95664 distinct valuesHigh cardinality
order_estimated_delivery_date has a high cardinality: 459 distinct valuesHigh cardinality
review_creation_date has a high cardinality: 636 distinct valuesHigh cardinality
review_answer_timestamp has a high cardinality: 98248 distinct valuesHigh cardinality
shipping_limit_date has a high cardinality: 93318 distinct valuesHigh cardinality
customer_city has a high cardinality: 4119 distinct valuesHigh cardinality
product_category_name_english has a high cardinality: 72 distinct valuesHigh cardinality
seller_city has a high cardinality: 611 distinct valuesHigh cardinality
payment_value is highly overall correlated with priceHigh correlation
price is highly overall correlated with payment_value and 1 other fieldsHigh correlation
product_weight_g is highly overall correlated with price and 3 other fieldsHigh correlation
product_length_cm is highly overall correlated with product_weight_g and 1 other fieldsHigh correlation
product_height_cm is highly overall correlated with product_weight_gHigh correlation
product_width_cm is highly overall correlated with product_weight_g and 1 other fieldsHigh correlation
order_status is highly imbalanced (91.6%)Imbalance
payment_type is highly imbalanced (52.6%)Imbalance
seller_state is highly imbalanced (63.3%)Imbalance
order_delivered_carrier_date has 2086 (1.8%) missing valuesMissing
order_delivered_customer_date has 3421 (2.9%) missing valuesMissing
order_purchase_timestamp is uniformly distributedUniform
order_approved_at is uniformly distributedUniform
order_delivered_customer_date is uniformly distributedUniform
review_answer_timestamp is uniformly distributedUniform
shipping_limit_date is uniformly distributedUniform
length_comment_title has 104159 (87.4%) zerosZeros
length_comment_message has 67932 (57.0%) zerosZeros
product_description_lenght has 1709 (1.4%) zerosZeros
product_photos_qty has 1709 (1.4%) zerosZeros

Reproduction

Analysis started2023-02-08 13:42:44.376976
Analysis finished2023-02-08 13:43:28.016804
Duration43.64 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

order_status
Categorical

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
delivered
115723 
shipped
 
1256
canceled
 
750
unavailable
 
652
invoiced
 
378
Other values (3)
 
384

Length

Max length11
Median length9
Mean length8.9834401
Min length7

Characters and Unicode

Total characters1070314
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdelivered
2nd rowdelivered
3rd rowdelivered
4th rowdelivered
5th rowdelivered

Common Values

ValueCountFrequency (%)
delivered 115723
97.1%
shipped 1256
 
1.1%
canceled 750
 
0.6%
unavailable 652
 
0.5%
invoiced 378
 
0.3%
processing 376
 
0.3%
created 5
 
< 0.1%
approved 3
 
< 0.1%

Length

2023-02-08T14:43:28.098137image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-08T14:43:28.259699image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
delivered 115723
97.1%
shipped 1256
 
1.1%
canceled 750
 
0.6%
unavailable 652
 
0.5%
invoiced 378
 
0.3%
processing 376
 
0.3%
created 5
 
< 0.1%
approved 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 351344
32.8%
d 233838
21.8%
i 118763
 
11.1%
l 117777
 
11.0%
v 116756
 
10.9%
r 116107
 
10.8%
p 2894
 
0.3%
a 2714
 
0.3%
c 2259
 
0.2%
n 2156
 
0.2%
Other values (7) 5706
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1070314
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 351344
32.8%
d 233838
21.8%
i 118763
 
11.1%
l 117777
 
11.0%
v 116756
 
10.9%
r 116107
 
10.8%
p 2894
 
0.3%
a 2714
 
0.3%
c 2259
 
0.2%
n 2156
 
0.2%
Other values (7) 5706
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 1070314
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 351344
32.8%
d 233838
21.8%
i 118763
 
11.1%
l 117777
 
11.0%
v 116756
 
10.9%
r 116107
 
10.8%
p 2894
 
0.3%
a 2714
 
0.3%
c 2259
 
0.2%
n 2156
 
0.2%
Other values (7) 5706
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1070314
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 351344
32.8%
d 233838
21.8%
i 118763
 
11.1%
l 117777
 
11.0%
v 116756
 
10.9%
r 116107
 
10.8%
p 2894
 
0.3%
a 2714
 
0.3%
c 2259
 
0.2%
n 2156
 
0.2%
Other values (7) 5706
 
0.5%

order_purchase_timestamp
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct98875
Distinct (%)83.0%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2017-08-08 20:26:31
 
63
2017-09-23 14:56:45
 
38
2017-04-20 12:45:34
 
29
2017-06-07 12:05:10
 
26
2018-05-12 12:28:58
 
24
Other values (98870)
118963 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters2263717
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique85645 ?
Unique (%)71.9%

Sample

1st row2017-10-02 10:56:33
2nd row2017-10-02 10:56:33
3rd row2017-10-02 10:56:33
4th row2017-08-15 18:29:31
5th row2017-08-02 18:24:47

Common Values

ValueCountFrequency (%)
2017-08-08 20:26:31 63
 
0.1%
2017-09-23 14:56:45 38
 
< 0.1%
2017-04-20 12:45:34 29
 
< 0.1%
2017-06-07 12:05:10 26
 
< 0.1%
2018-05-12 12:28:58 24
 
< 0.1%
2018-02-14 16:34:27 24
 
< 0.1%
2017-07-07 14:55:43 24
 
< 0.1%
2017-03-09 23:39:26 24
 
< 0.1%
2017-11-25 13:54:39 24
 
< 0.1%
2017-12-08 12:00:04 22
 
< 0.1%
Other values (98865) 118845
99.7%

Length

2023-02-08T14:43:28.394516image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2017-11-24 1435
 
0.6%
2017-11-25 640
 
0.3%
2017-11-27 508
 
0.2%
2017-11-26 485
 
0.2%
2017-11-28 453
 
0.2%
2018-08-06 445
 
0.2%
2018-08-07 444
 
0.2%
2018-05-15 441
 
0.2%
2018-05-07 430
 
0.2%
2018-05-14 425
 
0.2%
Other values (51442) 232580
97.6%

Most occurring characters

ValueCountFrequency (%)
1 368631
16.3%
0 366708
16.2%
2 290748
12.8%
- 238286
10.5%
: 238286
10.5%
8 123332
 
5.4%
119143
 
5.3%
7 111011
 
4.9%
3 105264
 
4.7%
5 96250
 
4.3%
Other values (3) 206058
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1668002
73.7%
Dash Punctuation 238286
 
10.5%
Other Punctuation 238286
 
10.5%
Space Separator 119143
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 368631
22.1%
0 366708
22.0%
2 290748
17.4%
8 123332
 
7.4%
7 111011
 
6.7%
3 105264
 
6.3%
5 96250
 
5.8%
4 96104
 
5.8%
6 56686
 
3.4%
9 53268
 
3.2%
Dash Punctuation
ValueCountFrequency (%)
- 238286
100.0%
Other Punctuation
ValueCountFrequency (%)
: 238286
100.0%
Space Separator
ValueCountFrequency (%)
119143
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2263717
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 368631
16.3%
0 366708
16.2%
2 290748
12.8%
- 238286
10.5%
: 238286
10.5%
8 123332
 
5.4%
119143
 
5.3%
7 111011
 
4.9%
3 105264
 
4.7%
5 96250
 
4.3%
Other values (3) 206058
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2263717
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 368631
16.3%
0 366708
16.2%
2 290748
12.8%
- 238286
10.5%
: 238286
10.5%
8 123332
 
5.4%
119143
 
5.3%
7 111011
 
4.9%
3 105264
 
4.7%
5 96250
 
4.3%
Other values (3) 206058
9.1%

order_approved_at
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct90733
Distinct (%)76.3%
Missing177
Missing (%)0.1%
Memory size1.8 MiB
2017-08-08 20:43:31
 
63
2017-09-25 17:44:41
 
38
2017-04-22 09:10:13
 
29
2017-06-09 16:15:08
 
26
2017-03-09 23:39:26
 
24
Other values (90728)
118786 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters2260354
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique72654 ?
Unique (%)61.1%

Sample

1st row2017-10-02 11:07:15
2nd row2017-10-02 11:07:15
3rd row2017-10-02 11:07:15
4th row2017-08-15 20:05:16
5th row2017-08-02 18:43:15

Common Values

ValueCountFrequency (%)
2017-08-08 20:43:31 63
 
0.1%
2017-09-25 17:44:41 38
 
< 0.1%
2017-04-22 09:10:13 29
 
< 0.1%
2017-06-09 16:15:08 26
 
< 0.1%
2017-03-09 23:39:26 24
 
< 0.1%
2018-05-12 15:41:58 24
 
< 0.1%
2017-07-07 15:10:17 24
 
< 0.1%
2018-02-21 12:28:15 24
 
< 0.1%
2017-11-25 14:16:34 24
 
< 0.1%
2018-02-24 03:20:27 23
 
< 0.1%
Other values (90723) 118667
99.6%
(Missing) 177
 
0.1%

Length

2023-02-08T14:43:28.517030image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2018-04-24 1163
 
0.5%
2017-11-24 1000
 
0.4%
2017-11-25 943
 
0.4%
2018-07-05 825
 
0.3%
2017-11-28 607
 
0.3%
2018-08-07 505
 
0.2%
2018-05-08 498
 
0.2%
2018-01-22 489
 
0.2%
2018-08-20 488
 
0.2%
2018-05-01 487
 
0.2%
Other values (42347) 230927
97.1%

Most occurring characters

ValueCountFrequency (%)
0 381200
16.9%
1 366273
16.2%
2 289265
12.8%
- 237932
10.5%
: 237932
10.5%
118966
 
5.3%
8 116954
 
5.2%
5 114527
 
5.1%
3 111559
 
4.9%
7 105817
 
4.7%
Other values (3) 179929
8.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1665524
73.7%
Dash Punctuation 237932
 
10.5%
Other Punctuation 237932
 
10.5%
Space Separator 118966
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 381200
22.9%
1 366273
22.0%
2 289265
17.4%
8 116954
 
7.0%
5 114527
 
6.9%
3 111559
 
6.7%
7 105817
 
6.4%
4 82414
 
4.9%
6 51368
 
3.1%
9 46147
 
2.8%
Dash Punctuation
ValueCountFrequency (%)
- 237932
100.0%
Other Punctuation
ValueCountFrequency (%)
: 237932
100.0%
Space Separator
ValueCountFrequency (%)
118966
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2260354
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 381200
16.9%
1 366273
16.2%
2 289265
12.8%
- 237932
10.5%
: 237932
10.5%
118966
 
5.3%
8 116954
 
5.2%
5 114527
 
5.1%
3 111559
 
4.9%
7 105817
 
4.7%
Other values (3) 179929
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2260354
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 381200
16.9%
1 366273
16.2%
2 289265
12.8%
- 237932
10.5%
: 237932
10.5%
118966
 
5.3%
8 116954
 
5.2%
5 114527
 
5.1%
3 111559
 
4.9%
7 105817
 
4.7%
Other values (3) 179929
8.0%

order_delivered_carrier_date
Categorical

HIGH CARDINALITY  MISSING 

Distinct81018
Distinct (%)69.2%
Missing2086
Missing (%)1.8%
Memory size1.8 MiB
2017-08-10 11:58:14
 
63
2018-05-09 15:48:00
 
48
2017-10-02 23:47:54
 
38
2018-05-10 18:29:00
 
36
2018-05-14 14:25:00
 
32
Other values (81013)
116840 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters2224083
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique61382 ?
Unique (%)52.4%

Sample

1st row2017-10-04 19:55:00
2nd row2017-10-04 19:55:00
3rd row2017-10-04 19:55:00
4th row2017-08-17 15:28:33
5th row2017-08-04 17:35:43

Common Values

ValueCountFrequency (%)
2017-08-10 11:58:14 63
 
0.1%
2018-05-09 15:48:00 48
 
< 0.1%
2017-10-02 23:47:54 38
 
< 0.1%
2018-05-10 18:29:00 36
 
< 0.1%
2018-05-14 14:25:00 32
 
< 0.1%
2017-04-24 11:31:17 29
 
< 0.1%
2018-05-04 15:46:00 29
 
< 0.1%
2018-08-08 15:01:00 27
 
< 0.1%
2017-06-16 15:50:28 26
 
< 0.1%
2018-06-13 14:13:00 24
 
< 0.1%
Other values (81008) 116705
98.0%
(Missing) 2086
 
1.8%

Length

2023-02-08T14:43:28.638869image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2017-11-28 925
 
0.4%
2017-11-27 818
 
0.3%
2017-11-29 706
 
0.3%
2018-02-27 632
 
0.3%
2018-03-27 622
 
0.3%
2018-08-06 595
 
0.3%
2017-11-30 584
 
0.2%
2018-05-14 568
 
0.2%
2018-08-13 550
 
0.2%
2018-05-03 539
 
0.2%
Other values (37539) 227575
97.2%

Most occurring characters

ValueCountFrequency (%)
0 405304
18.2%
1 346875
15.6%
2 275757
12.4%
- 234114
10.5%
: 234114
10.5%
8 123405
 
5.5%
117057
 
5.3%
7 106647
 
4.8%
3 98637
 
4.4%
4 92061
 
4.1%
Other values (3) 190112
8.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1638798
73.7%
Dash Punctuation 234114
 
10.5%
Other Punctuation 234114
 
10.5%
Space Separator 117057
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 405304
24.7%
1 346875
21.2%
2 275757
16.8%
8 123405
 
7.5%
7 106647
 
6.5%
3 98637
 
6.0%
4 92061
 
5.6%
5 89897
 
5.5%
6 51437
 
3.1%
9 48778
 
3.0%
Dash Punctuation
ValueCountFrequency (%)
- 234114
100.0%
Other Punctuation
ValueCountFrequency (%)
: 234114
100.0%
Space Separator
ValueCountFrequency (%)
117057
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2224083
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 405304
18.2%
1 346875
15.6%
2 275757
12.4%
- 234114
10.5%
: 234114
10.5%
8 123405
 
5.5%
117057
 
5.3%
7 106647
 
4.8%
3 98637
 
4.4%
4 92061
 
4.1%
Other values (3) 190112
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2224083
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 405304
18.2%
1 346875
15.6%
2 275757
12.4%
- 234114
10.5%
: 234114
10.5%
8 123405
 
5.5%
117057
 
5.3%
7 106647
 
4.8%
3 98637
 
4.4%
4 92061
 
4.1%
Other values (3) 190112
8.5%

order_delivered_customer_date
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct95664
Distinct (%)82.7%
Missing3421
Missing (%)2.9%
Memory size1.8 MiB
2017-08-14 12:46:18
 
63
2017-10-18 22:35:50
 
38
2017-06-22 16:04:46
 
26
2017-11-30 14:59:18
 
24
2018-02-28 20:09:19
 
24
Other values (95659)
115547 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters2198718
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique82390 ?
Unique (%)71.2%

Sample

1st row2017-10-10 21:25:13
2nd row2017-10-10 21:25:13
3rd row2017-10-10 21:25:13
4th row2017-08-18 14:44:43
5th row2017-08-07 18:30:01

Common Values

ValueCountFrequency (%)
2017-08-14 12:46:18 63
 
0.1%
2017-10-18 22:35:50 38
 
< 0.1%
2017-06-22 16:04:46 26
 
< 0.1%
2017-11-30 14:59:18 24
 
< 0.1%
2018-02-28 20:09:19 24
 
< 0.1%
2017-07-27 20:52:15 24
 
< 0.1%
2017-03-21 13:32:45 24
 
< 0.1%
2018-06-01 15:18:45 24
 
< 0.1%
2017-12-21 16:33:10 22
 
< 0.1%
2017-10-22 14:43:54 22
 
< 0.1%
Other values (95654) 115431
96.9%
(Missing) 3421
 
2.9%

Length

2023-02-08T14:43:28.759215image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2018-05-21 529
 
0.2%
2018-05-14 528
 
0.2%
2018-08-13 507
 
0.2%
2018-05-18 505
 
0.2%
2018-08-27 504
 
0.2%
2018-05-03 501
 
0.2%
2018-04-11 495
 
0.2%
2017-12-11 489
 
0.2%
2017-12-19 484
 
0.2%
2017-06-19 482
 
0.2%
Other values (41734) 226420
97.8%

Most occurring characters

ValueCountFrequency (%)
1 340122
15.5%
0 336673
15.3%
2 291213
13.2%
- 231444
10.5%
: 231444
10.5%
8 135442
 
6.2%
115722
 
5.3%
7 107273
 
4.9%
3 107058
 
4.9%
4 100137
 
4.6%
Other values (3) 202190
9.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1620108
73.7%
Dash Punctuation 231444
 
10.5%
Other Punctuation 231444
 
10.5%
Space Separator 115722
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 340122
21.0%
0 336673
20.8%
2 291213
18.0%
8 135442
 
8.4%
7 107273
 
6.6%
3 107058
 
6.6%
4 100137
 
6.2%
5 93750
 
5.8%
6 58489
 
3.6%
9 49951
 
3.1%
Dash Punctuation
ValueCountFrequency (%)
- 231444
100.0%
Other Punctuation
ValueCountFrequency (%)
: 231444
100.0%
Space Separator
ValueCountFrequency (%)
115722
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2198718
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 340122
15.5%
0 336673
15.3%
2 291213
13.2%
- 231444
10.5%
: 231444
10.5%
8 135442
 
6.2%
115722
 
5.3%
7 107273
 
4.9%
3 107058
 
4.9%
4 100137
 
4.6%
Other values (3) 202190
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2198718
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 340122
15.5%
0 336673
15.3%
2 291213
13.2%
- 231444
10.5%
: 231444
10.5%
8 135442
 
6.2%
115722
 
5.3%
7 107273
 
4.9%
3 107058
 
4.9%
4 100137
 
4.6%
Other values (3) 202190
9.2%
Distinct459
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2017-12-20 00:00:00
 
663
2018-03-13 00:00:00
 
620
2018-03-12 00:00:00
 
618
2018-05-29 00:00:00
 
612
2018-05-30 00:00:00
 
591
Other values (454)
116039 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters2263717
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)< 0.1%

Sample

1st row2017-10-18 00:00:00
2nd row2017-10-18 00:00:00
3rd row2017-10-18 00:00:00
4th row2017-08-28 00:00:00
5th row2017-08-15 00:00:00

Common Values

ValueCountFrequency (%)
2017-12-20 00:00:00 663
 
0.6%
2018-03-13 00:00:00 620
 
0.5%
2018-03-12 00:00:00 618
 
0.5%
2018-05-29 00:00:00 612
 
0.5%
2018-05-30 00:00:00 591
 
0.5%
2018-02-14 00:00:00 590
 
0.5%
2018-07-16 00:00:00 590
 
0.5%
2018-07-05 00:00:00 588
 
0.5%
2018-02-06 00:00:00 587
 
0.5%
2017-12-19 00:00:00 587
 
0.5%
Other values (449) 113097
94.9%

Length

2023-02-08T14:43:28.888210image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:00:00 119143
50.0%
2017-12-20 663
 
0.3%
2018-03-13 620
 
0.3%
2018-03-12 618
 
0.3%
2018-05-29 612
 
0.3%
2018-05-30 591
 
0.2%
2018-02-14 590
 
0.2%
2018-07-16 590
 
0.2%
2018-07-05 588
 
0.2%
2018-02-06 587
 
0.2%
Other values (450) 113684
47.7%

Most occurring characters

ValueCountFrequency (%)
0 985406
43.5%
- 238286
 
10.5%
: 238286
 
10.5%
1 204158
 
9.0%
2 185778
 
8.2%
119143
 
5.3%
8 98222
 
4.3%
7 72819
 
3.2%
3 31950
 
1.4%
5 24449
 
1.1%
Other values (3) 65220
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1668002
73.7%
Dash Punctuation 238286
 
10.5%
Other Punctuation 238286
 
10.5%
Space Separator 119143
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 985406
59.1%
1 204158
 
12.2%
2 185778
 
11.1%
8 98222
 
5.9%
7 72819
 
4.4%
3 31950
 
1.9%
5 24449
 
1.5%
6 23221
 
1.4%
4 22239
 
1.3%
9 19760
 
1.2%
Dash Punctuation
ValueCountFrequency (%)
- 238286
100.0%
Other Punctuation
ValueCountFrequency (%)
: 238286
100.0%
Space Separator
ValueCountFrequency (%)
119143
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2263717
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 985406
43.5%
- 238286
 
10.5%
: 238286
 
10.5%
1 204158
 
9.0%
2 185778
 
8.2%
119143
 
5.3%
8 98222
 
4.3%
7 72819
 
3.2%
3 31950
 
1.4%
5 24449
 
1.1%
Other values (3) 65220
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2263717
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 985406
43.5%
- 238286
 
10.5%
: 238286
 
10.5%
1 204158
 
9.0%
2 185778
 
8.2%
119143
 
5.3%
8 98222
 
4.3%
7 72819
 
3.2%
3 31950
 
1.4%
5 24449
 
1.1%
Other values (3) 65220
 
2.9%

review_score
Categorical

Distinct5
Distinct (%)< 0.1%
Missing997
Missing (%)0.8%
Memory size1.8 MiB
5.0
66343 
4.0
22319 
1.0
15428 
3.0
9894 
2.0
 
4162

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters354438
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row4.0
3rd row4.0
4th row4.0
5th row5.0

Common Values

ValueCountFrequency (%)
5.0 66343
55.7%
4.0 22319
 
18.7%
1.0 15428
 
12.9%
3.0 9894
 
8.3%
2.0 4162
 
3.5%
(Missing) 997
 
0.8%

Length

2023-02-08T14:43:29.003832image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-08T14:43:29.140263image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
5.0 66343
56.2%
4.0 22319
 
18.9%
1.0 15428
 
13.1%
3.0 9894
 
8.4%
2.0 4162
 
3.5%

Most occurring characters

ValueCountFrequency (%)
. 118146
33.3%
0 118146
33.3%
5 66343
18.7%
4 22319
 
6.3%
1 15428
 
4.4%
3 9894
 
2.8%
2 4162
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 236292
66.7%
Other Punctuation 118146
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 118146
50.0%
5 66343
28.1%
4 22319
 
9.4%
1 15428
 
6.5%
3 9894
 
4.2%
2 4162
 
1.8%
Other Punctuation
ValueCountFrequency (%)
. 118146
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 354438
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 118146
33.3%
0 118146
33.3%
5 66343
18.7%
4 22319
 
6.3%
1 15428
 
4.4%
3 9894
 
2.8%
2 4162
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 354438
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 118146
33.3%
0 118146
33.3%
5 66343
18.7%
4 22319
 
6.3%
1 15428
 
4.4%
3 9894
 
2.8%
2 4162
 
1.2%
Distinct636
Distinct (%)0.5%
Missing997
Missing (%)0.8%
Memory size1.8 MiB
2017-12-19 00:00:00
 
547
2018-05-22 00:00:00
 
521
2018-05-15 00:00:00
 
517
2017-12-20 00:00:00
 
515
2018-05-19 00:00:00
 
509
Other values (631)
115537 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters2244774
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)< 0.1%

Sample

1st row2017-10-11 00:00:00
2nd row2017-10-11 00:00:00
3rd row2017-10-11 00:00:00
4th row2017-08-19 00:00:00
5th row2017-08-08 00:00:00

Common Values

ValueCountFrequency (%)
2017-12-19 00:00:00 547
 
0.5%
2018-05-22 00:00:00 521
 
0.4%
2018-05-15 00:00:00 517
 
0.4%
2017-12-20 00:00:00 515
 
0.4%
2018-05-19 00:00:00 509
 
0.4%
2018-08-28 00:00:00 507
 
0.4%
2018-03-29 00:00:00 504
 
0.4%
2018-05-04 00:00:00 504
 
0.4%
2018-04-12 00:00:00 486
 
0.4%
2018-03-30 00:00:00 485
 
0.4%
Other values (626) 113051
94.9%
(Missing) 997
 
0.8%

Length

2023-02-08T14:43:29.260638image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:00:00 118049
50.0%
2017-12-19 547
 
0.2%
2018-05-22 521
 
0.2%
2018-05-15 517
 
0.2%
2017-12-20 515
 
0.2%
2018-05-19 509
 
0.2%
2018-08-28 507
 
0.2%
2018-03-29 504
 
0.2%
2018-05-04 504
 
0.2%
2018-04-12 486
 
0.2%
Other values (628) 113633
48.1%

Most occurring characters

ValueCountFrequency (%)
0 973738
43.4%
- 236292
 
10.5%
: 236292
 
10.5%
1 205175
 
9.1%
2 188753
 
8.4%
118146
 
5.3%
8 94367
 
4.2%
7 74321
 
3.3%
3 29057
 
1.3%
5 25233
 
1.1%
Other values (3) 63400
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1654044
73.7%
Dash Punctuation 236292
 
10.5%
Other Punctuation 236292
 
10.5%
Space Separator 118146
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 973738
58.9%
1 205175
 
12.4%
2 188753
 
11.4%
8 94367
 
5.7%
7 74321
 
4.5%
3 29057
 
1.8%
5 25233
 
1.5%
4 23694
 
1.4%
6 23414
 
1.4%
9 16292
 
1.0%
Dash Punctuation
ValueCountFrequency (%)
- 236292
100.0%
Other Punctuation
ValueCountFrequency (%)
: 236292
100.0%
Space Separator
ValueCountFrequency (%)
118146
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2244774
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 973738
43.4%
- 236292
 
10.5%
: 236292
 
10.5%
1 205175
 
9.1%
2 188753
 
8.4%
118146
 
5.3%
8 94367
 
4.2%
7 74321
 
3.3%
3 29057
 
1.3%
5 25233
 
1.1%
Other values (3) 63400
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2244774
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 973738
43.4%
- 236292
 
10.5%
: 236292
 
10.5%
1 205175
 
9.1%
2 188753
 
8.4%
118146
 
5.3%
8 94367
 
4.2%
7 74321
 
3.3%
3 29057
 
1.3%
5 25233
 
1.1%
Other values (3) 63400
 
2.8%

review_answer_timestamp
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct98248
Distinct (%)83.2%
Missing997
Missing (%)0.8%
Memory size1.8 MiB
2017-08-17 22:17:55
 
63
2017-10-19 21:08:44
 
38
2017-05-24 16:21:27
 
29
2017-06-28 18:49:50
 
26
2018-03-07 15:08:10
 
24
Other values (98243)
117966 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters2244774
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique85133 ?
Unique (%)72.1%

Sample

1st row2017-10-12 03:43:48
2nd row2017-10-12 03:43:48
3rd row2017-10-12 03:43:48
4th row2017-08-20 15:16:36
5th row2017-08-08 23:26:23

Common Values

ValueCountFrequency (%)
2017-08-17 22:17:55 63
 
0.1%
2017-10-19 21:08:44 38
 
< 0.1%
2017-05-24 16:21:27 29
 
< 0.1%
2017-06-28 18:49:50 26
 
< 0.1%
2018-03-07 15:08:10 24
 
< 0.1%
2018-06-04 19:04:20 24
 
< 0.1%
2017-03-23 08:34:13 24
 
< 0.1%
2017-08-01 13:20:31 24
 
< 0.1%
2017-12-22 20:33:52 22
 
< 0.1%
2018-02-19 20:04:36 21
 
< 0.1%
Other values (98238) 117851
98.9%
(Missing) 997
 
0.8%

Length

2023-02-08T14:43:29.388848image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2018-05-20 781
 
0.3%
2018-05-21 684
 
0.3%
2018-05-10 593
 
0.3%
2017-12-20 472
 
0.2%
2018-04-13 432
 
0.2%
2017-12-13 421
 
0.2%
2017-12-22 421
 
0.2%
2018-08-24 418
 
0.2%
2018-05-11 414
 
0.2%
2017-12-21 412
 
0.2%
Other values (53983) 231244
97.9%

Most occurring characters

ValueCountFrequency (%)
0 378113
16.8%
1 350346
15.6%
2 299408
13.3%
- 236292
10.5%
: 236292
10.5%
8 124163
 
5.5%
118146
 
5.3%
3 109822
 
4.9%
7 102251
 
4.6%
5 93511
 
4.2%
Other values (3) 196430
8.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1654044
73.7%
Dash Punctuation 236292
 
10.5%
Other Punctuation 236292
 
10.5%
Space Separator 118146
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 378113
22.9%
1 350346
21.2%
2 299408
18.1%
8 124163
 
7.5%
3 109822
 
6.6%
7 102251
 
6.2%
5 93511
 
5.7%
4 93373
 
5.6%
6 53342
 
3.2%
9 49715
 
3.0%
Dash Punctuation
ValueCountFrequency (%)
- 236292
100.0%
Other Punctuation
ValueCountFrequency (%)
: 236292
100.0%
Space Separator
ValueCountFrequency (%)
118146
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2244774
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 378113
16.8%
1 350346
15.6%
2 299408
13.3%
- 236292
10.5%
: 236292
10.5%
8 124163
 
5.5%
118146
 
5.3%
3 109822
 
4.9%
7 102251
 
4.6%
5 93511
 
4.2%
Other values (3) 196430
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2244774
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 378113
16.8%
1 350346
15.6%
2 299408
13.3%
- 236292
10.5%
: 236292
10.5%
8 124163
 
5.5%
118146
 
5.3%
3 109822
 
4.9%
7 102251
 
4.6%
5 93511
 
4.2%
Other values (3) 196430
8.8%

length_comment_title
Real number (ℝ)

Distinct27
Distinct (%)< 0.1%
Missing997
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean1.4256682
Minimum0
Maximum26
Zeros104159
Zeros (%)87.4%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-02-08T14:43:29.508940image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile13
Maximum26
Range26
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.4658155
Coefficient of variation (CV)3.1324367
Kurtosis11.168711
Mean1.4256682
Median Absolute Deviation (MAD)0
Skewness3.3991733
Sum168437
Variance19.943508
MonotonicityNot monotonic
2023-02-08T14:43:29.653209image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 104159
87.4%
9 2347
 
2.0%
5 1259
 
1.1%
15 1041
 
0.9%
3 804
 
0.7%
10 685
 
0.6%
13 618
 
0.5%
17 616
 
0.5%
25 575
 
0.5%
16 528
 
0.4%
Other values (17) 5514
 
4.6%
(Missing) 997
 
0.8%
ValueCountFrequency (%)
0 104159
87.4%
1 191
 
0.2%
2 300
 
0.3%
3 804
 
0.7%
4 226
 
0.2%
5 1259
 
1.1%
6 282
 
0.2%
7 450
 
0.4%
8 407
 
0.3%
9 2347
 
2.0%
ValueCountFrequency (%)
26 2
 
< 0.1%
25 575
0.5%
24 299
0.3%
23 253
0.2%
22 265
0.2%
21 319
0.3%
20 484
0.4%
19 331
0.3%
18 413
0.3%
17 616
0.5%

length_comment_message
Real number (ℝ)

Distinct209
Distinct (%)0.2%
Missing997
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean29.929579
Minimum0
Maximum208
Zeros67932
Zeros (%)57.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-02-08T14:43:29.832222image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q344
95-th percentile154
Maximum208
Range208
Interquartile range (IQR)44

Descriptive statistics

Standard deviation49.834722
Coefficient of variation (CV)1.6650659
Kurtosis2.8705156
Mean29.929579
Median Absolute Deviation (MAD)0
Skewness1.8936024
Sum3536060
Variance2483.4995
MonotonicityNot monotonic
2023-02-08T14:43:29.980303image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 67932
57.0%
9 1144
 
1.0%
200 806
 
0.7%
5 651
 
0.5%
26 617
 
0.5%
10 594
 
0.5%
20 584
 
0.5%
3 571
 
0.5%
30 561
 
0.5%
35 547
 
0.5%
Other values (199) 44139
37.0%
(Missing) 997
 
0.8%
ValueCountFrequency (%)
0 67932
57.0%
1 109
 
0.1%
2 233
 
0.2%
3 571
 
0.5%
4 129
 
0.1%
5 651
 
0.5%
6 214
 
0.2%
7 285
 
0.2%
8 294
 
0.2%
9 1144
 
1.0%
ValueCountFrequency (%)
208 1
 
< 0.1%
207 1
 
< 0.1%
206 1
 
< 0.1%
205 1
 
< 0.1%
204 17
 
< 0.1%
203 21
 
< 0.1%
202 20
 
< 0.1%
201 29
 
< 0.1%
200 806
0.7%
199 452
0.4%

payment_type
Categorical

Distinct5
Distinct (%)< 0.1%
Missing3
Missing (%)< 0.1%
Memory size1.8 MiB
credit_card
87776 
boleto
23190 
voucher
 
6465
debit_card
 
1706
not_defined
 
3

Length

Max length11
Median length11
Mean length9.7954004
Min length6

Characters and Unicode

Total characters1167024
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcredit_card
2nd rowvoucher
3rd rowvoucher
4th rowcredit_card
5th rowcredit_card

Common Values

ValueCountFrequency (%)
credit_card 87776
73.7%
boleto 23190
 
19.5%
voucher 6465
 
5.4%
debit_card 1706
 
1.4%
not_defined 3
 
< 0.1%
(Missing) 3
 
< 0.1%

Length

2023-02-08T14:43:30.121063image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-08T14:43:30.251893image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
credit_card 87776
73.7%
boleto 23190
 
19.5%
voucher 6465
 
5.4%
debit_card 1706
 
1.4%
not_defined 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
c 183723
15.7%
r 183723
15.7%
d 178970
15.3%
e 119143
10.2%
t 112675
9.7%
i 89485
7.7%
_ 89485
7.7%
a 89482
7.7%
o 52848
 
4.5%
b 24896
 
2.1%
Other values (6) 42594
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1077539
92.3%
Connector Punctuation 89485
 
7.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 183723
17.1%
r 183723
17.1%
d 178970
16.6%
e 119143
11.1%
t 112675
10.5%
i 89485
8.3%
a 89482
8.3%
o 52848
 
4.9%
b 24896
 
2.3%
l 23190
 
2.2%
Other values (5) 19404
 
1.8%
Connector Punctuation
ValueCountFrequency (%)
_ 89485
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1077539
92.3%
Common 89485
 
7.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 183723
17.1%
r 183723
17.1%
d 178970
16.6%
e 119143
11.1%
t 112675
10.5%
i 89485
8.3%
a 89482
8.3%
o 52848
 
4.9%
b 24896
 
2.3%
l 23190
 
2.2%
Other values (5) 19404
 
1.8%
Common
ValueCountFrequency (%)
_ 89485
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1167024
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c 183723
15.7%
r 183723
15.7%
d 178970
15.3%
e 119143
10.2%
t 112675
9.7%
i 89485
7.7%
_ 89485
7.7%
a 89482
7.7%
o 52848
 
4.5%
b 24896
 
2.1%
Other values (6) 42594
 
3.6%

payment_installments
Real number (ℝ)

Distinct24
Distinct (%)< 0.1%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2.9412456
Minimum0
Maximum24
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-02-08T14:43:30.369787image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q34
95-th percentile10
Maximum24
Range24
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.7778477
Coefficient of variation (CV)0.94444604
Kurtosis2.5065453
Mean2.9412456
Median Absolute Deviation (MAD)1
Skewness1.6198199
Sum350420
Variance7.7164381
MonotonicityNot monotonic
2023-02-08T14:43:30.500367image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1 59446
49.9%
2 13838
 
11.6%
3 11889
 
10.0%
4 8072
 
6.8%
10 6976
 
5.9%
5 6097
 
5.1%
8 5120
 
4.3%
6 4674
 
3.9%
7 1848
 
1.6%
9 739
 
0.6%
Other values (14) 441
 
0.4%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 59446
49.9%
2 13838
 
11.6%
3 11889
 
10.0%
4 8072
 
6.8%
5 6097
 
5.1%
6 4674
 
3.9%
7 1848
 
1.6%
8 5120
 
4.3%
9 739
 
0.6%
ValueCountFrequency (%)
24 34
 
< 0.1%
23 1
 
< 0.1%
22 1
 
< 0.1%
21 6
 
< 0.1%
20 21
 
< 0.1%
18 38
< 0.1%
17 8
 
< 0.1%
16 7
 
< 0.1%
15 93
0.1%
14 16
 
< 0.1%

payment_value
Real number (ℝ)

Distinct29077
Distinct (%)24.4%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean172.73514
Minimum0
Maximum13664.08
Zeros9
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-02-08T14:43:30.648504image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile27.1
Q160.85
median108.16
Q3189.24
95-th percentile515.93
Maximum13664.08
Range13664.08
Interquartile range (IQR)128.39

Descriptive statistics

Standard deviation267.77608
Coefficient of variation (CV)1.550212
Kurtosis500.3632
Mean172.73514
Median Absolute Deviation (MAD)56.64
Skewness13.965989
Sum20579664
Variance71704.027
MonotonicityNot monotonic
2023-02-08T14:43:30.801291image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 351
 
0.3%
100 300
 
0.3%
20 286
 
0.2%
77.57 250
 
0.2%
35 166
 
0.1%
73.34 160
 
0.1%
30 139
 
0.1%
116.94 133
 
0.1%
56.78 123
 
0.1%
107.78 120
 
0.1%
Other values (29067) 117112
98.3%
ValueCountFrequency (%)
0 9
< 0.1%
0.01 6
< 0.1%
0.03 2
 
< 0.1%
0.05 2
 
< 0.1%
0.07 1
 
< 0.1%
0.08 2
 
< 0.1%
0.09 1
 
< 0.1%
0.1 3
 
< 0.1%
0.11 2
 
< 0.1%
0.13 2
 
< 0.1%
ValueCountFrequency (%)
13664.08 8
< 0.1%
7274.88 4
< 0.1%
6929.31 1
 
< 0.1%
6922.21 1
 
< 0.1%
6726.66 1
 
< 0.1%
6081.54 6
< 0.1%
4950.34 1
 
< 0.1%
4809.44 2
 
< 0.1%
4764.34 1
 
< 0.1%
4681.78 1
 
< 0.1%

shipping_limit_date
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct93318
Distinct (%)78.9%
Missing833
Missing (%)0.7%
Memory size1.8 MiB
2017-08-14 20:43:31
 
63
2017-10-05 17:44:41
 
38
2017-04-27 09:10:13
 
29
2017-06-15 16:15:08
 
26
2017-11-30 14:16:34
 
24
Other values (93313)
118130 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters2247890
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique76920 ?
Unique (%)65.0%

Sample

1st row2017-10-06 11:07:15
2nd row2017-10-06 11:07:15
3rd row2017-10-06 11:07:15
4th row2017-08-21 20:05:16
5th row2017-08-08 18:37:31

Common Values

ValueCountFrequency (%)
2017-08-14 20:43:31 63
 
0.1%
2017-10-05 17:44:41 38
 
< 0.1%
2017-04-27 09:10:13 29
 
< 0.1%
2017-06-15 16:15:08 26
 
< 0.1%
2017-11-30 14:16:34 24
 
< 0.1%
2018-05-15 15:30:28 24
 
< 0.1%
2018-02-27 12:28:15 24
 
< 0.1%
2017-07-13 15:10:17 24
 
< 0.1%
2017-03-15 23:39:26 24
 
< 0.1%
2017-10-24 13:06:21 22
 
< 0.1%
Other values (93308) 118012
99.1%
(Missing) 833
 
0.7%

Length

2023-02-08T14:43:30.952149image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2017-11-30 1753
 
0.7%
2017-12-07 794
 
0.3%
2018-04-19 729
 
0.3%
2018-05-10 696
 
0.3%
2018-01-18 689
 
0.3%
2018-03-08 681
 
0.3%
2018-08-07 678
 
0.3%
2018-02-22 673
 
0.3%
2018-03-22 670
 
0.3%
2018-03-01 663
 
0.3%
Other values (40675) 228594
96.6%

Most occurring characters

ValueCountFrequency (%)
0 379298
16.9%
1 364436
16.2%
2 289766
12.9%
- 236620
10.5%
: 236620
10.5%
8 118667
 
5.3%
118310
 
5.3%
3 114161
 
5.1%
5 111987
 
5.0%
7 102121
 
4.5%
Other values (3) 175904
7.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1656340
73.7%
Dash Punctuation 236620
 
10.5%
Other Punctuation 236620
 
10.5%
Space Separator 118310
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 379298
22.9%
1 364436
22.0%
2 289766
17.5%
8 118667
 
7.2%
3 114161
 
6.9%
5 111987
 
6.8%
7 102121
 
6.2%
4 79394
 
4.8%
6 49607
 
3.0%
9 46903
 
2.8%
Dash Punctuation
ValueCountFrequency (%)
- 236620
100.0%
Other Punctuation
ValueCountFrequency (%)
: 236620
100.0%
Space Separator
ValueCountFrequency (%)
118310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2247890
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 379298
16.9%
1 364436
16.2%
2 289766
12.9%
- 236620
10.5%
: 236620
10.5%
8 118667
 
5.3%
118310
 
5.3%
3 114161
 
5.1%
5 111987
 
5.0%
7 102121
 
4.5%
Other values (3) 175904
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2247890
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 379298
16.9%
1 364436
16.2%
2 289766
12.9%
- 236620
10.5%
: 236620
10.5%
8 118667
 
5.3%
118310
 
5.3%
3 114161
 
5.1%
5 111987
 
5.0%
7 102121
 
4.5%
Other values (3) 175904
7.8%

price
Real number (ℝ)

Distinct5968
Distinct (%)5.0%
Missing833
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean120.6466
Minimum0.85
Maximum6735
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-02-08T14:43:31.093652image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.85
5-th percentile17
Q139.9
median74.9
Q3134.9
95-th percentile349.9
Maximum6735
Range6734.15
Interquartile range (IQR)95

Descriptive statistics

Standard deviation184.10969
Coefficient of variation (CV)1.5260247
Kurtosis119.15494
Mean120.6466
Median Absolute Deviation (MAD)42
Skewness7.8925735
Sum14273700
Variance33896.378
MonotonicityNot monotonic
2023-02-08T14:43:31.248387image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59.9 2619
 
2.2%
69.9 2113
 
1.8%
49.9 2051
 
1.7%
89.9 1644
 
1.4%
99.9 1526
 
1.3%
39.9 1403
 
1.2%
29.9 1387
 
1.2%
19.9 1284
 
1.1%
79.9 1282
 
1.1%
29.99 1228
 
1.0%
Other values (5958) 101773
85.4%
ValueCountFrequency (%)
0.85 3
 
< 0.1%
1.2 20
< 0.1%
2.2 2
 
< 0.1%
2.29 1
 
< 0.1%
2.9 1
 
< 0.1%
2.99 1
 
< 0.1%
3 2
 
< 0.1%
3.06 3
 
< 0.1%
3.49 3
 
< 0.1%
3.5 7
 
< 0.1%
ValueCountFrequency (%)
6735 1
< 0.1%
6729 1
< 0.1%
6499 1
< 0.1%
4799 1
< 0.1%
4690 1
< 0.1%
4590 1
< 0.1%
4399.87 1
< 0.1%
4099.99 1
< 0.1%
4059 1
< 0.1%
3999.9 1
< 0.1%

freight_value
Real number (ℝ)

Distinct6999
Distinct (%)5.9%
Missing833
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean20.032387
Minimum0
Maximum409.68
Zeros390
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-02-08T14:43:31.417086image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.78
Q113.08
median16.28
Q321.18
95-th percentile45.3
Maximum409.68
Range409.68
Interquartile range (IQR)8.1

Descriptive statistics

Standard deviation15.83685
Coefficient of variation (CV)0.79056234
Kurtosis57.635327
Mean20.032387
Median Absolute Deviation (MAD)3.63
Skewness5.5433839
Sum2370031.6
Variance250.80583
MonotonicityNot monotonic
2023-02-08T14:43:31.971596image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.1 3861
 
3.2%
7.78 2355
 
2.0%
11.85 1999
 
1.7%
14.1 1992
 
1.7%
18.23 1632
 
1.4%
7.39 1573
 
1.3%
16.11 1211
 
1.0%
15.23 1064
 
0.9%
8.72 970
 
0.8%
16.79 930
 
0.8%
Other values (6989) 100723
84.5%
ValueCountFrequency (%)
0 390
0.3%
0.01 4
 
< 0.1%
0.02 3
 
< 0.1%
0.03 14
 
< 0.1%
0.04 4
 
< 0.1%
0.05 9
 
< 0.1%
0.06 13
 
< 0.1%
0.07 1
 
< 0.1%
0.08 12
 
< 0.1%
0.09 6
 
< 0.1%
ValueCountFrequency (%)
409.68 1
< 0.1%
375.28 2
< 0.1%
339.59 1
< 0.1%
338.3 1
< 0.1%
322.1 1
< 0.1%
321.88 1
< 0.1%
321.46 1
< 0.1%
317.47 1
< 0.1%
314.4 1
< 0.1%
314.02 1
< 0.1%

customer_city
Categorical

Distinct4119
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
sao paulo
18875 
rio de janeiro
 
8311
belo horizonte
 
3299
brasilia
 
2500
curitiba
 
1827
Other values (4114)
84331 

Length

Max length32
Median length27
Mean length10.33532
Min length3

Characters and Unicode

Total characters1231381
Distinct characters31
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1036 ?
Unique (%)0.9%

Sample

1st rowsao paulo
2nd rowsao paulo
3rd rowsao paulo
4th rowsao paulo
5th rowsao paulo

Common Values

ValueCountFrequency (%)
sao paulo 18875
 
15.8%
rio de janeiro 8311
 
7.0%
belo horizonte 3299
 
2.8%
brasilia 2500
 
2.1%
curitiba 1827
 
1.5%
campinas 1757
 
1.5%
porto alegre 1675
 
1.4%
salvador 1544
 
1.3%
guarulhos 1415
 
1.2%
sao bernardo do campo 1131
 
0.9%
Other values (4109) 76809
64.5%

Length

2023-02-08T14:43:32.128114image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sao 25445
 
12.2%
paulo 18957
 
9.1%
de 11657
 
5.6%
rio 9967
 
4.8%
janeiro 8311
 
4.0%
do 5095
 
2.4%
belo 3373
 
1.6%
horizonte 3327
 
1.6%
brasilia 2510
 
1.2%
porto 1998
 
1.0%
Other values (3285) 118347
56.6%

Most occurring characters

ValueCountFrequency (%)
a 203082
16.5%
o 151991
12.3%
i 94150
 
7.6%
r 91236
 
7.4%
89844
 
7.3%
e 79953
 
6.5%
s 75446
 
6.1%
n 54566
 
4.4%
u 54064
 
4.4%
l 53676
 
4.4%
Other values (21) 283373
23.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1140982
92.7%
Space Separator 89844
 
7.3%
Dash Punctuation 290
 
< 0.1%
Other Punctuation 263
 
< 0.1%
Decimal Number 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 203082
17.8%
o 151991
13.3%
i 94150
 
8.3%
r 91236
 
8.0%
e 79953
 
7.0%
s 75446
 
6.6%
n 54566
 
4.8%
u 54064
 
4.7%
l 53676
 
4.7%
p 44782
 
3.9%
Other values (16) 238036
20.9%
Decimal Number
ValueCountFrequency (%)
1 1
50.0%
4 1
50.0%
Space Separator
ValueCountFrequency (%)
89844
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 290
100.0%
Other Punctuation
ValueCountFrequency (%)
' 263
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1140982
92.7%
Common 90399
 
7.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 203082
17.8%
o 151991
13.3%
i 94150
 
8.3%
r 91236
 
8.0%
e 79953
 
7.0%
s 75446
 
6.6%
n 54566
 
4.8%
u 54064
 
4.7%
l 53676
 
4.7%
p 44782
 
3.9%
Other values (16) 238036
20.9%
Common
ValueCountFrequency (%)
89844
99.4%
- 290
 
0.3%
' 263
 
0.3%
1 1
 
< 0.1%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1231381
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 203082
16.5%
o 151991
12.3%
i 94150
 
7.6%
r 91236
 
7.4%
89844
 
7.3%
e 79953
 
6.5%
s 75446
 
6.1%
n 54566
 
4.4%
u 54064
 
4.4%
l 53676
 
4.4%
Other values (21) 283373
23.0%

customer_state
Categorical

Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
SP
50265 
RJ
15518 
MG
13819 
RS
6573 
PR
6043 
Other values (22)
26925 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters238286
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSP
2nd rowSP
3rd rowSP
4th rowSP
5th rowSP

Common Values

ValueCountFrequency (%)
SP 50265
42.2%
RJ 15518
 
13.0%
MG 13819
 
11.6%
RS 6573
 
5.5%
PR 6043
 
5.1%
SC 4345
 
3.6%
BA 4091
 
3.4%
DF 2516
 
2.1%
GO 2466
 
2.1%
ES 2360
 
2.0%
Other values (17) 11147
 
9.4%

Length

2023-02-08T14:43:32.257378image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sp 50265
42.2%
rj 15518
 
13.0%
mg 13819
 
11.6%
rs 6573
 
5.5%
pr 6043
 
5.1%
sc 4345
 
3.6%
ba 4091
 
3.4%
df 2516
 
2.1%
go 2466
 
2.1%
es 2360
 
2.0%
Other values (17) 11147
 
9.4%

Most occurring characters

ValueCountFrequency (%)
S 64808
27.2%
P 60647
25.5%
R 29104
12.2%
M 16842
 
7.1%
G 16285
 
6.8%
J 15518
 
6.5%
A 6892
 
2.9%
E 6234
 
2.6%
C 6005
 
2.5%
B 4735
 
2.0%
Other values (7) 11216
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 238286
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 64808
27.2%
P 60647
25.5%
R 29104
12.2%
M 16842
 
7.1%
G 16285
 
6.8%
J 15518
 
6.5%
A 6892
 
2.9%
E 6234
 
2.6%
C 6005
 
2.5%
B 4735
 
2.0%
Other values (7) 11216
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 238286
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 64808
27.2%
P 60647
25.5%
R 29104
12.2%
M 16842
 
7.1%
G 16285
 
6.8%
J 15518
 
6.5%
A 6892
 
2.9%
E 6234
 
2.6%
C 6005
 
2.5%
B 4735
 
2.0%
Other values (7) 11216
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 238286
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 64808
27.2%
P 60647
25.5%
R 29104
12.2%
M 16842
 
7.1%
G 16285
 
6.8%
J 15518
 
6.5%
A 6892
 
2.9%
E 6234
 
2.6%
C 6005
 
2.5%
B 4735
 
2.0%
Other values (7) 11216
 
4.7%
Distinct2961
Distinct (%)2.5%
Missing833
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean774.61444
Minimum0
Maximum3992
Zeros1709
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-02-08T14:43:32.399167image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile135
Q1340
median594
Q3977
95-th percentile2113
Maximum3992
Range3992
Interquartile range (IQR)637

Descriptive statistics

Standard deviation654.60589
Coefficient of variation (CV)0.84507319
Kurtosis4.8856742
Mean774.61444
Median Absolute Deviation (MAD)299
Skewness1.9918057
Sum91644634
Variance428508.87
MonotonicityNot monotonic
2023-02-08T14:43:32.565131image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1709
 
1.4%
341 711
 
0.6%
1893 667
 
0.6%
348 648
 
0.5%
903 594
 
0.5%
492 594
 
0.5%
245 587
 
0.5%
366 537
 
0.5%
236 516
 
0.4%
340 487
 
0.4%
Other values (2951) 111260
93.4%
(Missing) 833
 
0.7%
ValueCountFrequency (%)
0 1709
1.4%
4 6
 
< 0.1%
8 2
 
< 0.1%
15 1
 
< 0.1%
20 7
 
< 0.1%
23 1
 
< 0.1%
26 2
 
< 0.1%
27 4
 
< 0.1%
28 2
 
< 0.1%
30 8
 
< 0.1%
ValueCountFrequency (%)
3992 2
 
< 0.1%
3988 1
 
< 0.1%
3985 3
< 0.1%
3976 6
< 0.1%
3963 1
 
< 0.1%
3956 3
< 0.1%
3954 2
 
< 0.1%
3950 2
 
< 0.1%
3949 1
 
< 0.1%
3948 1
 
< 0.1%

product_photos_qty
Real number (ℝ)

Distinct20
Distinct (%)< 0.1%
Missing833
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean2.1733074
Minimum0
Maximum20
Zeros1709
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-02-08T14:43:32.717576image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q33
95-th percentile6
Maximum20
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7251844
Coefficient of variation (CV)0.79380599
Kurtosis4.7453186
Mean2.1733074
Median Absolute Deviation (MAD)1
Skewness1.8812261
Sum257124
Variance2.9762614
MonotonicityNot monotonic
2023-02-08T14:43:32.842224image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1 58957
49.5%
2 23054
 
19.3%
3 12978
 
10.9%
4 8863
 
7.4%
5 5599
 
4.7%
6 3945
 
3.3%
0 1709
 
1.4%
7 1560
 
1.3%
8 774
 
0.6%
10 354
 
0.3%
Other values (10) 517
 
0.4%
(Missing) 833
 
0.7%
ValueCountFrequency (%)
0 1709
 
1.4%
1 58957
49.5%
2 23054
 
19.3%
3 12978
 
10.9%
4 8863
 
7.4%
5 5599
 
4.7%
6 3945
 
3.3%
7 1560
 
1.3%
8 774
 
0.6%
9 318
 
0.3%
ValueCountFrequency (%)
20 1
 
< 0.1%
19 2
 
< 0.1%
18 4
 
< 0.1%
17 11
 
< 0.1%
15 12
 
< 0.1%
14 6
 
< 0.1%
13 30
 
< 0.1%
12 60
 
0.1%
11 73
 
0.1%
10 354
0.3%

product_weight_g
Real number (ℝ)

Distinct2204
Distinct (%)1.9%
Missing853
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean2112.2507
Minimum0
Maximum40425
Zeros8
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-02-08T14:43:33.003728image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile125
Q1300
median700
Q31800
95-th percentile9850
Maximum40425
Range40425
Interquartile range (IQR)1500

Descriptive statistics

Standard deviation3786.6951
Coefficient of variation (CV)1.7927299
Kurtosis16.01826
Mean2112.2507
Median Absolute Deviation (MAD)500
Skewness3.5830918
Sum2.4985814 × 108
Variance14339060
MonotonicityNot monotonic
2023-02-08T14:43:33.172117image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200 7093
 
6.0%
150 5410
 
4.5%
250 4741
 
4.0%
300 4429
 
3.7%
400 3787
 
3.2%
100 3666
 
3.1%
350 3291
 
2.8%
500 2856
 
2.4%
600 2838
 
2.4%
700 2148
 
1.8%
Other values (2194) 78031
65.5%
ValueCountFrequency (%)
0 8
 
< 0.1%
2 5
 
< 0.1%
25 3
 
< 0.1%
50 991
0.8%
53 2
 
< 0.1%
54 2
 
< 0.1%
55 2
 
< 0.1%
58 1
 
< 0.1%
60 9
 
< 0.1%
61 5
 
< 0.1%
ValueCountFrequency (%)
40425 3
 
< 0.1%
30000 303
0.3%
29800 1
 
< 0.1%
29750 1
 
< 0.1%
29700 4
 
< 0.1%
29600 5
 
< 0.1%
29500 2
 
< 0.1%
29250 1
 
< 0.1%
29150 1
 
< 0.1%
29100 1
 
< 0.1%

product_length_cm
Real number (ℝ)

Distinct99
Distinct (%)0.1%
Missing853
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean30.265145
Minimum7
Maximum105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-02-08T14:43:33.350740image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile16
Q118
median25
Q338
95-th percentile62
Maximum105
Range98
Interquartile range (IQR)20

Descriptive statistics

Standard deviation16.189367
Coefficient of variation (CV)0.53491788
Kurtosis3.6785662
Mean30.265145
Median Absolute Deviation (MAD)8
Skewness1.7456849
Sum3580064
Variance262.09561
MonotonicityNot monotonic
2023-02-08T14:43:33.504387image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 18363
 
15.4%
20 10999
 
9.2%
30 7951
 
6.7%
17 6202
 
5.2%
18 5909
 
5.0%
19 4898
 
4.1%
25 4871
 
4.1%
40 4360
 
3.7%
22 4000
 
3.4%
50 3163
 
2.7%
Other values (89) 47574
39.9%
ValueCountFrequency (%)
7 32
 
< 0.1%
8 2
 
< 0.1%
9 4
 
< 0.1%
10 8
 
< 0.1%
11 96
 
0.1%
12 41
 
< 0.1%
13 60
 
0.1%
14 138
 
0.1%
15 220
 
0.2%
16 18363
15.4%
ValueCountFrequency (%)
105 335
0.3%
104 35
 
< 0.1%
103 46
 
< 0.1%
102 60
 
0.1%
101 108
 
0.1%
100 429
0.4%
99 36
 
< 0.1%
98 50
 
< 0.1%
97 11
 
< 0.1%
96 8
 
< 0.1%

product_height_cm
Real number (ℝ)

Distinct102
Distinct (%)0.1%
Missing853
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean16.619706
Minimum2
Maximum105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-02-08T14:43:33.671584image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q18
median13
Q320
95-th percentile45
Maximum105
Range103
Interquartile range (IQR)12

Descriptive statistics

Standard deviation13.453584
Coefficient of variation (CV)0.80949592
Kurtosis7.2778781
Mean16.619706
Median Absolute Deviation (MAD)6
Skewness2.2389625
Sum1965945
Variance180.99892
MonotonicityNot monotonic
2023-02-08T14:43:33.836221image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 10374
 
8.7%
20 6915
 
5.8%
15 6896
 
5.8%
12 6520
 
5.5%
11 6432
 
5.4%
2 5254
 
4.4%
4 4910
 
4.1%
8 4873
 
4.1%
5 4776
 
4.0%
16 4765
 
4.0%
Other values (92) 56575
47.5%
ValueCountFrequency (%)
2 5254
4.4%
3 2821
 
2.4%
4 4910
4.1%
5 4776
4.0%
6 3576
 
3.0%
7 4387
3.7%
8 4873
4.1%
9 3408
 
2.9%
10 10374
8.7%
11 6432
5.4%
ValueCountFrequency (%)
105 139
0.1%
104 14
 
< 0.1%
103 49
 
< 0.1%
102 10
 
< 0.1%
100 43
 
< 0.1%
99 5
 
< 0.1%
98 3
 
< 0.1%
97 2
 
< 0.1%
96 8
 
< 0.1%
95 22
 
< 0.1%

product_width_cm
Real number (ℝ)

Distinct95
Distinct (%)0.1%
Missing853
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean23.074799
Minimum6
Maximum118
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-02-08T14:43:33.997238image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile11
Q115
median20
Q330
95-th percentile45
Maximum118
Range112
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.749139
Coefficient of variation (CV)0.50917622
Kurtosis4.5530162
Mean23.074799
Median Absolute Deviation (MAD)6
Skewness1.707171
Sum2729518
Variance138.04227
MonotonicityNot monotonic
2023-02-08T14:43:34.150078image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 12701
 
10.7%
11 11144
 
9.4%
15 9376
 
7.9%
16 8810
 
7.4%
30 8045
 
6.8%
12 5711
 
4.8%
13 5491
 
4.6%
14 4846
 
4.1%
18 4192
 
3.5%
40 4157
 
3.5%
Other values (85) 43817
36.8%
ValueCountFrequency (%)
6 2
 
< 0.1%
7 5
 
< 0.1%
8 29
 
< 0.1%
9 51
 
< 0.1%
10 83
 
0.1%
11 11144
9.4%
12 5711
4.8%
13 5491
4.6%
14 4846
4.1%
15 9376
7.9%
ValueCountFrequency (%)
118 8
 
< 0.1%
105 14
 
< 0.1%
104 1
 
< 0.1%
103 1
 
< 0.1%
102 2
 
< 0.1%
101 2
 
< 0.1%
100 43
< 0.1%
98 1
 
< 0.1%
97 1
 
< 0.1%
95 2
 
< 0.1%
Distinct72
Distinct (%)0.1%
Missing833
Missing (%)0.7%
Memory size1.8 MiB
bed_bath_table
11988 
health_beauty
10032 
sports_leisure
9004 
furniture_decor
8832 
computers_accessories
8150 
Other values (67)
70304 

Length

Max length39
Median length31
Mean length12.900532
Min length3

Characters and Unicode

Total characters1526262
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhousewares
2nd rowhousewares
3rd rowhousewares
4th rowhousewares
5th rowhousewares

Common Values

ValueCountFrequency (%)
bed_bath_table 11988
 
10.1%
health_beauty 10032
 
8.4%
sports_leisure 9004
 
7.6%
furniture_decor 8832
 
7.4%
computers_accessories 8150
 
6.8%
housewares 7380
 
6.2%
watches_gifts 6213
 
5.2%
telephony 4726
 
4.0%
garden_tools 4590
 
3.9%
auto 4400
 
3.7%
Other values (62) 42995
36.1%

Length

2023-02-08T14:43:34.312144image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bed_bath_table 11988
 
10.1%
health_beauty 10032
 
8.5%
sports_leisure 9004
 
7.6%
furniture_decor 8832
 
7.5%
computers_accessories 8150
 
6.9%
housewares 7380
 
6.2%
watches_gifts 6213
 
5.3%
telephony 4726
 
4.0%
garden_tools 4590
 
3.9%
auto 4400
 
3.7%
Other values (62) 42995
36.3%

Most occurring characters

ValueCountFrequency (%)
e 185298
12.1%
s 142057
 
9.3%
t 133341
 
8.7%
o 113626
 
7.4%
r 105808
 
6.9%
a 102466
 
6.7%
_ 102216
 
6.7%
u 79961
 
5.2%
c 72479
 
4.7%
i 63331
 
4.1%
Other values (15) 425679
27.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1423745
93.3%
Connector Punctuation 102216
 
6.7%
Decimal Number 301
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 185298
13.0%
s 142057
 
10.0%
t 133341
 
9.4%
o 113626
 
8.0%
r 105808
 
7.4%
a 102466
 
7.2%
u 79961
 
5.6%
c 72479
 
5.1%
i 63331
 
4.4%
h 59668
 
4.2%
Other values (13) 365710
25.7%
Connector Punctuation
ValueCountFrequency (%)
_ 102216
100.0%
Decimal Number
ValueCountFrequency (%)
2 301
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1423745
93.3%
Common 102517
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 185298
13.0%
s 142057
 
10.0%
t 133341
 
9.4%
o 113626
 
8.0%
r 105808
 
7.4%
a 102466
 
7.2%
u 79961
 
5.6%
c 72479
 
5.1%
i 63331
 
4.4%
h 59668
 
4.2%
Other values (13) 365710
25.7%
Common
ValueCountFrequency (%)
_ 102216
99.7%
2 301
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1526262
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 185298
12.1%
s 142057
 
9.3%
t 133341
 
8.7%
o 113626
 
7.4%
r 105808
 
6.9%
a 102466
 
6.7%
_ 102216
 
6.7%
u 79961
 
5.2%
c 72479
 
4.7%
i 63331
 
4.1%
Other values (15) 425679
27.9%

seller_city
Categorical

Distinct611
Distinct (%)0.5%
Missing833
Missing (%)0.7%
Memory size1.8 MiB
sao paulo
29293 
ibitinga
8373 
curitiba
 
3161
santo andre
 
3149
sao jose do rio preto
 
2693
Other values (606)
71641 

Length

Max length40
Median length31
Mean length10.102451
Min length2

Characters and Unicode

Total characters1195221
Distinct characters41
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique64 ?
Unique (%)0.1%

Sample

1st rowmaua
2nd rowmaua
3rd rowmaua
4th rowmaua
5th rowmaua

Common Values

ValueCountFrequency (%)
sao paulo 29293
24.6%
ibitinga 8373
 
7.0%
curitiba 3161
 
2.7%
santo andre 3149
 
2.6%
sao jose do rio preto 2693
 
2.3%
belo horizonte 2688
 
2.3%
rio de janeiro 2535
 
2.1%
guarulhos 2456
 
2.1%
ribeirao preto 2374
 
2.0%
maringa 2292
 
1.9%
Other values (601) 59296
49.8%

Length

2023-02-08T14:43:34.481071image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sao 36362
 
17.9%
paulo 29574
 
14.5%
ibitinga 8373
 
4.1%
rio 5930
 
2.9%
do 5524
 
2.7%
preto 5518
 
2.7%
de 4192
 
2.1%
jose 4085
 
2.0%
santo 3270
 
1.6%
andre 3164
 
1.6%
Other values (640) 97267
47.9%

Most occurring characters

ValueCountFrequency (%)
a 198772
16.6%
o 146215
12.2%
i 102111
 
8.5%
85009
 
7.1%
r 78220
 
6.5%
s 76216
 
6.4%
e 64170
 
5.4%
u 62907
 
5.3%
p 58419
 
4.9%
l 56917
 
4.8%
Other values (31) 266265
22.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1108993
92.8%
Space Separator 85009
 
7.1%
Other Punctuation 614
 
0.1%
Modifier Symbol 369
 
< 0.1%
Dash Punctuation 164
 
< 0.1%
Close Punctuation 31
 
< 0.1%
Open Punctuation 31
 
< 0.1%
Decimal Number 8
 
< 0.1%
Nonspacing Mark 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 198772
17.9%
o 146215
13.2%
i 102111
9.2%
r 78220
 
7.1%
s 76216
 
6.9%
e 64170
 
5.8%
u 62907
 
5.7%
p 58419
 
5.3%
l 56917
 
5.1%
t 47317
 
4.3%
Other values (14) 217729
19.6%
Other Punctuation
ValueCountFrequency (%)
' 347
56.5%
/ 141
23.0%
. 76
 
12.4%
@ 38
 
6.2%
\ 6
 
1.0%
, 6
 
1.0%
Decimal Number
ValueCountFrequency (%)
4 2
25.0%
2 2
25.0%
5 2
25.0%
0 1
12.5%
8 1
12.5%
Space Separator
ValueCountFrequency (%)
85009
100.0%
Modifier Symbol
ValueCountFrequency (%)
´ 369
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 164
100.0%
Close Punctuation
ValueCountFrequency (%)
) 31
100.0%
Open Punctuation
ValueCountFrequency (%)
( 31
100.0%
Nonspacing Mark
ValueCountFrequency (%)
̃ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1108993
92.8%
Common 86226
 
7.2%
Inherited 2
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 198772
17.9%
o 146215
13.2%
i 102111
9.2%
r 78220
 
7.1%
s 76216
 
6.9%
e 64170
 
5.8%
u 62907
 
5.7%
p 58419
 
5.3%
l 56917
 
5.1%
t 47317
 
4.3%
Other values (14) 217729
19.6%
Common
ValueCountFrequency (%)
85009
98.6%
´ 369
 
0.4%
' 347
 
0.4%
- 164
 
0.2%
/ 141
 
0.2%
. 76
 
0.1%
@ 38
 
< 0.1%
) 31
 
< 0.1%
( 31
 
< 0.1%
\ 6
 
< 0.1%
Other values (6) 14
 
< 0.1%
Inherited
ValueCountFrequency (%)
̃ 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1194850
> 99.9%
None 369
 
< 0.1%
Diacriticals 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 198772
16.6%
o 146215
12.2%
i 102111
 
8.5%
85009
 
7.1%
r 78220
 
6.5%
s 76216
 
6.4%
e 64170
 
5.4%
u 62907
 
5.3%
p 58419
 
4.9%
l 56917
 
4.8%
Other values (29) 265894
22.3%
None
ValueCountFrequency (%)
´ 369
100.0%
Diacriticals
ValueCountFrequency (%)
̃ 2
100.0%

seller_state
Categorical

Distinct23
Distinct (%)< 0.1%
Missing833
Missing (%)0.7%
Memory size1.8 MiB
SP
84377 
MG
9314 
PR
9096 
RJ
 
5036
SC
 
4271
Other values (18)
 
6216

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters236620
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowSP
2nd rowSP
3rd rowSP
4th rowSP
5th rowSP

Common Values

ValueCountFrequency (%)
SP 84377
70.8%
MG 9314
 
7.8%
PR 9096
 
7.6%
RJ 5036
 
4.2%
SC 4271
 
3.6%
RS 2294
 
1.9%
DF 949
 
0.8%
BA 700
 
0.6%
GO 550
 
0.5%
PE 465
 
0.4%
Other values (13) 1258
 
1.1%
(Missing) 833
 
0.7%

Length

2023-02-08T14:43:34.630478image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sp 84377
71.3%
mg 9314
 
7.9%
pr 9096
 
7.7%
rj 5036
 
4.3%
sc 4271
 
3.6%
rs 2294
 
1.9%
df 949
 
0.8%
ba 700
 
0.6%
go 550
 
0.5%
pe 465
 
0.4%
Other values (13) 1258
 
1.1%

Most occurring characters

ValueCountFrequency (%)
P 94002
39.7%
S 91402
38.6%
R 16496
 
7.0%
M 9934
 
4.2%
G 9864
 
4.2%
J 5036
 
2.1%
C 4375
 
1.8%
A 1122
 
0.5%
E 968
 
0.4%
D 949
 
0.4%
Other values (6) 2472
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 236620
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 94002
39.7%
S 91402
38.6%
R 16496
 
7.0%
M 9934
 
4.2%
G 9864
 
4.2%
J 5036
 
2.1%
C 4375
 
1.8%
A 1122
 
0.5%
E 968
 
0.4%
D 949
 
0.4%
Other values (6) 2472
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 236620
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 94002
39.7%
S 91402
38.6%
R 16496
 
7.0%
M 9934
 
4.2%
G 9864
 
4.2%
J 5036
 
2.1%
C 4375
 
1.8%
A 1122
 
0.5%
E 968
 
0.4%
D 949
 
0.4%
Other values (6) 2472
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 236620
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 94002
39.7%
S 91402
38.6%
R 16496
 
7.0%
M 9934
 
4.2%
G 9864
 
4.2%
J 5036
 
2.1%
C 4375
 
1.8%
A 1122
 
0.5%
E 968
 
0.4%
D 949
 
0.4%
Other values (6) 2472
 
1.0%

Interactions

2023-02-08T14:43:22.597906image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:01.515768image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:03.285897image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:05.112718image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:07.027539image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:09.176467image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:11.166394image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:12.933077image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:14.876186image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:16.762990image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:18.956726image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:20.795852image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:22.776290image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:01.650071image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:03.423442image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:05.248711image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:07.184358image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:09.352061image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:11.299442image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:13.113059image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:15.036022image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:16.907829image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:19.108158image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:20.935613image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:22.976149image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:01.781791image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:03.559142image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:05.384221image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:07.331346image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:09.487419image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:11.435324image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:13.256178image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:15.177825image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:17.051248image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:19.254836image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:21.069738image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:23.125377image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:01.928054image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:03.705336image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:05.546070image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:07.493069image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:09.636467image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:11.574066image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:13.406178image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:15.321710image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:17.210092image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:19.404106image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:21.209066image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:23.300150image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:02.075032image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:03.864789image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:05.728218image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:07.899129image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:09.796138image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:11.722327image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:13.568925image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:15.478526image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:17.371326image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:19.567952image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:21.382319image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:23.481270image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:02.222750image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:04.009537image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:05.872038image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:08.066189image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:09.968111image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:11.895139image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:13.725565image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:15.635787image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:17.527275image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:19.740938image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:21.558014image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:23.616352image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:02.367115image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:04.143880image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:06.006481image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:08.203893image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:10.148459image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:12.052498image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:13.875561image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:15.799522image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:17.669375image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:19.873847image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:21.694975image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:23.781712image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:02.531199image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:04.300807image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:06.163414image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:08.362822image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:10.310884image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:12.206032image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:14.054408image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:15.959238image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:17.833601image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:20.025708image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:21.853541image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:23.947578image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:02.697395image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:04.454241image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:06.324973image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:08.519252image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:10.477347image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:12.359852image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:14.224710image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:16.144677image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:18.003371image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:20.192949image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:22.016521image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:24.125189image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:02.867306image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:04.625111image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:06.553565image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:08.703394image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:10.638365image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:12.514945image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:14.388852image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:16.322248image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:18.499829image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:20.354831image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:22.169378image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:24.255404image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:03.010006image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:04.760772image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:06.738980image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:08.886227image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:10.802156image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:12.648776image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:14.555123image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:16.469166image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:18.650961image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:20.512045image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:22.300176image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:24.388643image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:03.147718image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:04.931734image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:06.885156image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:09.032645image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:10.999489image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:12.788771image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:14.728870image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:16.617106image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:18.797571image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:20.658470image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-08T14:43:22.446024image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-02-08T14:43:34.763715image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
length_comment_titlelength_comment_messagepayment_installmentspayment_valuepricefreight_valueproduct_description_lenghtproduct_photos_qtyproduct_weight_gproduct_length_cmproduct_height_cmproduct_width_cmorder_statusreview_scorepayment_typecustomer_stateproduct_category_name_englishseller_state
length_comment_title1.0000.3120.0100.0430.0170.0450.0290.005-0.010-0.0320.002-0.0160.0210.0820.0290.0160.0380.023
length_comment_message0.3121.0000.0480.0830.0290.036-0.010-0.0090.0370.0100.0240.0160.0440.2160.0090.0250.0270.013
payment_installments0.0100.0481.0000.3950.3150.1910.0340.0000.1980.1090.1060.1250.0050.0270.2360.0320.0880.033
payment_value0.0430.0830.3951.0000.7890.4230.167-0.0080.4490.2290.3050.2320.0150.0280.0180.0290.1360.036
price0.0170.0290.3150.7891.0000.4330.2060.0300.5140.2660.3270.2710.0140.0120.0140.0200.1140.052
freight_value0.0450.0360.1910.4230.4331.0000.1190.0170.4480.2840.2840.2750.0150.0150.0090.0850.0950.048
product_description_lenght0.029-0.0100.0340.1670.2060.1191.0000.1520.096-0.0140.135-0.0700.0160.0150.0210.0260.2120.112
product_photos_qty0.005-0.0090.000-0.0080.0300.0170.1521.0000.0080.012-0.072-0.0060.0120.0160.0050.0140.1540.040
product_weight_g-0.0100.0370.1980.4490.5140.4480.0960.0081.0000.6190.5320.6210.0110.0200.0180.0280.1990.078
product_length_cm-0.0320.0100.1090.2290.2660.284-0.0140.0120.6191.0000.2480.6320.0140.0180.0220.0170.2580.084
product_height_cm0.0020.0240.1060.3050.3270.2840.135-0.0720.5320.2481.0000.3380.0160.0180.0150.0190.2780.065
product_width_cm-0.0160.0160.1250.2320.2710.275-0.070-0.0060.6210.6320.3381.0000.0040.0140.0200.0160.2910.057
order_status0.0210.0440.0050.0150.0140.0150.0160.0120.0110.0140.0160.0041.0000.1490.0370.0260.0300.029
review_score0.0820.2160.0270.0280.0120.0150.0150.0160.0200.0180.0180.0140.1491.0000.0100.0480.0540.023
payment_type0.0290.0090.2360.0180.0140.0090.0210.0050.0180.0220.0150.0200.0370.0101.0000.0330.0590.021
customer_state0.0160.0250.0320.0290.0200.0850.0260.0140.0280.0170.0190.0160.0260.0480.0331.0000.0370.053
product_category_name_english0.0380.0270.0880.1360.1140.0950.2120.1540.1990.2580.2780.2910.0300.0540.0590.0371.0000.176
seller_state0.0230.0130.0330.0360.0520.0480.1120.0400.0780.0840.0650.0570.0290.0230.0210.0530.1761.000

Missing values

2023-02-08T14:43:24.888477image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-08T14:43:25.812229image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-02-08T14:43:27.188547image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

order_statusorder_purchase_timestamporder_approved_atorder_delivered_carrier_dateorder_delivered_customer_dateorder_estimated_delivery_datereview_scorereview_creation_datereview_answer_timestamplength_comment_titlelength_comment_messagepayment_typepayment_installmentspayment_valueshipping_limit_datepricefreight_valuecustomer_citycustomer_stateproduct_description_lenghtproduct_photos_qtyproduct_weight_gproduct_length_cmproduct_height_cmproduct_width_cmproduct_category_name_englishseller_cityseller_state
0delivered2017-10-02 10:56:332017-10-02 11:07:152017-10-04 19:55:002017-10-10 21:25:132017-10-18 00:00:004.02017-10-11 00:00:002017-10-12 03:43:480.0170.0credit_card1.018.122017-10-06 11:07:1529.998.72sao pauloSP268.04.0500.019.08.013.0housewaresmauaSP
1delivered2017-10-02 10:56:332017-10-02 11:07:152017-10-04 19:55:002017-10-10 21:25:132017-10-18 00:00:004.02017-10-11 00:00:002017-10-12 03:43:480.0170.0voucher1.02.002017-10-06 11:07:1529.998.72sao pauloSP268.04.0500.019.08.013.0housewaresmauaSP
2delivered2017-10-02 10:56:332017-10-02 11:07:152017-10-04 19:55:002017-10-10 21:25:132017-10-18 00:00:004.02017-10-11 00:00:002017-10-12 03:43:480.0170.0voucher1.018.592017-10-06 11:07:1529.998.72sao pauloSP268.04.0500.019.08.013.0housewaresmauaSP
3delivered2017-08-15 18:29:312017-08-15 20:05:162017-08-17 15:28:332017-08-18 14:44:432017-08-28 00:00:004.02017-08-19 00:00:002017-08-20 15:16:360.084.0credit_card3.037.772017-08-21 20:05:1629.997.78sao pauloSP268.04.0500.019.08.013.0housewaresmauaSP
4delivered2017-08-02 18:24:472017-08-02 18:43:152017-08-04 17:35:432017-08-07 18:30:012017-08-15 00:00:005.02017-08-08 00:00:002017-08-08 23:26:230.060.0credit_card1.037.772017-08-08 18:37:3129.997.78sao pauloSP268.04.0500.019.08.013.0housewaresmauaSP
5delivered2017-10-23 23:26:462017-10-25 02:14:112017-10-27 16:48:462017-11-07 18:04:592017-11-13 00:00:003.02017-11-08 00:00:002017-11-10 19:52:380.00.0boleto1.044.092017-10-31 02:14:1129.9914.10florianopolisSC268.04.0500.019.08.013.0housewaresmauaSP
6delivered2017-08-10 13:35:552017-08-10 13:50:092017-08-11 13:52:352017-08-16 19:03:362017-08-23 00:00:005.02017-08-17 00:00:002017-08-21 12:43:270.041.0credit_card1.083.692017-08-16 13:50:0975.907.79itaquaquecetubaSP398.03.0238.020.010.015.0babymauaSP
7delivered2017-08-15 02:03:012017-08-15 02:15:132017-08-16 15:52:292017-08-25 21:59:262017-08-28 00:00:004.02017-08-26 00:00:002017-08-28 20:10:380.044.0credit_card2.083.692017-08-21 02:15:1375.907.79campinasSP398.03.0238.020.010.015.0babymauaSP
8delivered2017-08-01 16:31:352017-08-02 02:50:252017-08-03 14:36:342017-08-09 19:56:502017-08-23 00:00:005.02017-08-10 00:00:002017-08-11 21:08:380.00.0boleto1.090.182017-08-08 02:50:2575.9014.28curitibaPR398.03.0238.020.010.015.0babymauaSP
9delivered2017-08-10 14:04:582017-08-10 14:23:382017-08-11 13:52:352017-08-12 11:56:492017-08-23 00:00:002.02017-08-13 00:00:002017-08-14 12:24:580.084.0credit_card2.083.692017-08-16 14:23:3875.907.79campinasSP398.03.0238.020.010.015.0babymauaSP
order_statusorder_purchase_timestamporder_approved_atorder_delivered_carrier_dateorder_delivered_customer_dateorder_estimated_delivery_datereview_scorereview_creation_datereview_answer_timestamplength_comment_titlelength_comment_messagepayment_typepayment_installmentspayment_valueshipping_limit_datepricefreight_valuecustomer_citycustomer_stateproduct_description_lenghtproduct_photos_qtyproduct_weight_gproduct_length_cmproduct_height_cmproduct_width_cmproduct_category_name_englishseller_cityseller_state
119133canceled2018-03-01 11:42:232018-03-01 12:20:32NaNNaN2018-03-20 00:00:001.02018-03-22 00:00:002018-03-26 03:00:140.00.0credit_card2.0102.092018-03-08 12:20:3279.9022.19americanaSP87.01.03500.020.020.020.0furniture_decorpirassunungaSP
119134delivered2018-08-10 21:14:352018-08-10 21:25:222018-08-13 13:54:002018-08-21 04:16:312018-08-30 00:00:005.02018-08-21 00:00:002018-08-21 22:01:550.00.0credit_card2.0134.482018-08-15 21:25:2244.9922.25caraiMG645.02.0600.030.020.020.0sports_leisurepaulo lopesSC
119135delivered2018-08-10 21:14:352018-08-10 21:25:222018-08-13 13:54:002018-08-21 04:16:312018-08-30 00:00:005.02018-08-21 00:00:002018-08-21 22:01:550.00.0credit_card2.0134.482018-08-15 21:25:2244.9922.25caraiMG645.02.0600.030.020.020.0sports_leisurepaulo lopesSC
119136canceled2018-03-13 10:58:092018-03-14 03:08:35NaNNaN2018-03-23 00:00:005.02018-03-28 00:00:002018-03-28 18:11:450.00.0boleto1.0321.342018-03-20 03:08:35149.0011.67taboao da serraSP682.01.01700.030.05.030.0garden_toolssao pauloSP
119137canceled2018-03-13 10:58:092018-03-14 03:08:35NaNNaN2018-03-23 00:00:005.02018-03-28 00:00:002018-03-28 18:11:450.00.0boleto1.0321.342018-03-20 03:08:35149.0011.67taboao da serraSP682.01.01700.030.05.030.0garden_toolssao pauloSP
119138delivered2018-07-01 10:23:102018-07-05 16:17:522018-07-04 14:34:002018-07-09 15:06:572018-07-20 00:00:005.02018-07-10 00:00:002018-07-10 18:32:290.00.0boleto1.093.132018-07-10 08:32:3379.0014.13ferraz de vasconcelosSP516.02.0750.030.028.028.0construction_tools_lightsporto ferreiraSP
119139canceled2017-03-11 19:51:362017-03-11 19:51:36NaNNaN2017-03-30 00:00:001.02017-04-01 00:00:002017-04-01 10:24:030.00.0credit_card1.030.662017-03-16 19:51:3619.7010.96gasparSC260.02.0400.016.04.011.0autoblumenauSC
119140delivered2018-07-24 09:46:272018-07-24 11:24:272018-07-24 15:14:002018-08-02 22:47:352018-08-16 00:00:005.02018-08-03 00:00:002018-08-04 11:22:400.00.0debit_card1.0444.072018-07-30 11:24:27399.0045.07fortalezaCE729.02.02100.080.08.030.0furniture_decoramericanaSP
119141delivered2018-05-22 21:13:212018-05-22 21:35:402018-05-24 12:28:002018-06-12 23:11:292018-06-08 00:00:004.02018-06-10 00:00:002018-06-13 09:17:470.00.0credit_card4.0244.022018-05-28 21:31:24219.9024.12teofilo otoniMG531.01.05900.041.021.041.0furniture_decorsao pauloSP
119142delivered2018-05-15 17:41:002018-05-16 03:35:292018-05-16 17:20:002018-05-21 14:31:412018-05-29 00:00:005.02018-06-01 00:00:002018-06-01 15:14:230.00.0boleto1.028.292018-05-22 03:35:2915.5012.79sao bernardo do campoSP871.01.083.017.08.013.0perfumeryribeirao pretoSP

Duplicate rows

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order_statusorder_purchase_timestamporder_approved_atorder_delivered_carrier_dateorder_delivered_customer_dateorder_estimated_delivery_datereview_scorereview_creation_datereview_answer_timestamplength_comment_titlelength_comment_messagepayment_typepayment_installmentspayment_valueshipping_limit_datepricefreight_valuecustomer_citycustomer_stateproduct_description_lenghtproduct_photos_qtyproduct_weight_gproduct_length_cmproduct_height_cmproduct_width_cmproduct_category_name_englishseller_cityseller_state# duplicates
1698delivered2017-08-23 09:22:342017-08-24 14:30:232017-08-25 20:07:362017-09-02 12:13:032017-09-21 00:00:00NaNNaNNaNNaNNaNcredit_card4.02262.802017-08-30 14:30:2398.7014.44goianiaGO1042.01.01400.020.031.030.0autosao pauloSP20
4451delivered2018-02-22 15:30:412018-02-24 03:20:272018-03-02 00:18:012018-03-05 15:22:272018-03-08 00:00:001.02018-03-06 00:00:002018-03-12 12:46:070.00.0boleto1.02202.402018-03-01 02:50:48100.0010.12sao pauloSP452.01.0360.019.018.015.0computers_accessoriessao jose dos camposSP20
6487delivered2018-06-12 00:22:182018-06-12 01:35:102018-06-13 14:13:002018-06-14 14:58:592018-06-21 00:00:004.02018-06-15 00:00:002018-06-15 19:25:550.00.0voucher1.015.482018-06-18 01:30:5029.007.94pauliniaSP383.01.0100.020.015.012.0sports_leisureamparoSP18
1542delivered2017-08-08 20:26:312017-08-08 20:43:312017-08-10 11:58:142017-08-14 12:46:182017-08-30 00:00:005.02017-08-15 00:00:002017-08-17 22:17:550.00.0voucher1.016.702017-08-14 20:43:3112.9923.21sao pauloSP86.02.01300.050.09.041.0bed_bath_tabletres riosRJ16
136delivered2017-01-30 21:44:492017-01-30 22:33:452017-02-01 14:34:102017-02-14 10:48:102017-03-07 00:00:005.02017-02-15 00:00:002017-02-16 17:14:410.00.0credit_card10.0783.002017-02-03 21:44:4951.001.20goianiaGO369.02.0600.038.016.025.0garden_toolsjaragua do sulSC15
2851delivered2017-11-23 20:30:522017-11-24 10:31:102017-11-28 16:42:212017-12-13 20:19:352017-12-19 00:00:005.02017-12-14 00:00:002017-12-19 14:14:160.00.0credit_card10.01225.652017-11-30 10:30:5165.4916.22uniao da vitoriaPR1744.02.0700.062.015.015.0furniture_decorfernandopolisSP15
903delivered2017-05-29 14:06:332017-05-29 14:25:142017-05-30 09:25:022017-06-01 15:34:092017-06-09 00:00:005.02017-06-02 00:00:002017-06-03 17:09:010.00.0voucher1.050.002017-06-02 14:25:14723.7417.02sao pauloSP2396.02.03600.036.010.023.0musical_instrumentsbraganca paulistaSP14
1539delivered2017-08-08 20:26:312017-08-08 20:43:312017-08-10 11:58:142017-08-14 12:46:182017-08-30 00:00:005.02017-08-15 00:00:002017-08-17 22:17:550.00.0voucher1.02.612017-08-14 20:43:3112.9923.21sao pauloSP86.02.01300.050.09.041.0bed_bath_tabletres riosRJ14
3372delivered2017-12-13 14:21:152017-12-15 02:30:412017-12-15 18:45:182017-12-28 09:05:342018-01-08 00:00:001.02017-12-29 00:00:002017-12-31 12:08:240.00.0boleto1.01014.022017-12-21 02:30:4159.0013.43santosSP348.02.01550.030.022.030.0garden_toolssao jose do rio pretoSP14
4412delivered2018-02-21 11:45:072018-02-22 11:48:422018-02-27 18:27:012018-03-01 20:47:012018-03-07 00:00:001.02018-03-02 00:00:002018-03-03 00:44:540.074.0credit_card6.0528.782018-02-28 11:48:1229.997.78indaiatubaSP55.01.0300.020.020.013.0telephonysanto andreSP14